WO2022191210A1

WO2022191210A1 - System, method, and program for determining commodity or service suitable for user

Info

Publication number: WO2022191210A1
Application number: PCT/JP2022/010100
Authority: WO
Inventors: 秀樹藤井
Original assignee: 株式会社Advasa
Priority date: 2021-03-09
Filing date: 2022-03-08
Publication date: 2022-09-15
Also published as: JP2022137865A; JP2022138104A; JP7004886B1

Abstract

The present invention provides a system for determining a commodity or service suitable for a user. The system is configured to perform: acquisition of data and/or information regarding the user (step S701); calculation of a score that multi-dimensionally represents a feature of the user on the basis of the acquired data and/or information regarding the user (step S702); and determination of a commodity or service suitable for the user, by using the score (step S703).

Description

Systems, methods and programs for determining suitable goods or services for a user

The present invention relates to systems, methods, and programs for determining products or services suitable for users.

Systems that recommend products and services to users are known (for example, Patent Document 1).

JP 2008-198163 A

The purpose of the present invention is to provide a system or the like for determining products or services suitable for users by a new approach.

The present invention provides, for example, the following items.
(Item 1)
A system for determining products or services suitable for a user, comprising:
obtaining means for obtaining data and/or information about a user;
calculation means for calculating a score that multidimensionally represents the features of the user based on the acquired data and/or information about the user;
and determining means for using said score to determine suitable goods or services for said user.
(Item 2)
The determining means is
Deriving a feature amount according to the field of the product or the service from the score;
The system according to item 1, wherein the product or the service is determined based on the feature quantity.
(Item 3)
The system is a system for determining financial products suitable for a user,
3. The system according to item 2, wherein the feature amount includes a feature amount capable of evaluating the user's risk.
(Item 4)
The system is a system for determining insurance products suitable for a user,
4. The system according to item 3, wherein the feature amount includes a feature amount capable of evaluating health and/or financial risks of the user.
(Item 5)
The system is a system for determining a travel plan suitable for the user,
3. The system according to item 2, wherein the feature amount includes a feature amount capable of evaluating behavioral characteristics of the user.
(Item 6)
The system is a system for determining consumer goods suitable for a user,
3. The system according to item 2, wherein the feature amount includes a feature amount capable of evaluating the user's preference.
(Item 7)
7. The system according to any one of items 1 to 6, wherein the score includes a feature amount capable of evaluating whether the user is a trustworthy person.
(Item 8)
8. The system of item 7, wherein the score includes a personality feature, a money feature, and a health feature.
(Item 9)
The calculation means calculates the score according to the extent to which the acquired data and/or information on the user correlates with at least one of concepts related to personality, concepts related to money, and concepts related to health, items 1 to 8. A system according to any one of Claims 1 to 3.
(Item 10)
further comprising adjusting means for adjusting a range in which the obtaining means can obtain data and/or information about the user;
9. The system according to any one of items 1 to 8, wherein the calculation means calculates the score according to the obtainable range.
(Item 11)
11. The system of any one of items 1-10, wherein said data and/or information comprises information stored on a payroll card.
(Item 12)
further comprising writing means for writing the score to a token owned by the user;
12. A system according to items 1-11, wherein said determining means is arranged to be able to read said score from said token.
(Item 13)
13. The system of item 12, wherein the writing means is further configured to write information indicative of the determined goods or services into the token.
(Item 14)
14. The system according to any one of items 1 to 13, further comprising presenting means for presenting said determined goods or services to said user.
(Item 15)
A method for determining products or services suitable for a user, comprising:
obtaining data and/or information about the user;
calculating a score that multidimensionally represents the user's characteristics based on the acquired data and/or information about the user;
and using said score to determine suitable goods or services for said user.
(Item 15A)
16. The method of item 15, further comprising the features of any one or more of the above items.
(Item 16)
A program for determining goods or services suitable for a user, said program being executed on a computer system comprising a processor, said program comprising:
obtaining data and/or information about the user;
calculating a score that multidimensionally represents the user's characteristics based on the acquired data and/or information about the user;
and determining products or services suitable for the user using the score.
(Item 16A)
17. The program of item 16, further comprising the features of any one or more of the above items.
(Item 17)
A system for calculating a score used to determine products or services suitable for a user,
obtaining means for obtaining data and/or information about a user;
calculation means for calculating a score that multidimensionally represents the features of the user based on the acquired data and/or information about the user;
A system with
(Item 17A)
18. The system of item 17, further comprising the features of any one or more of the above items.
(Item 18)
A method for calculating a score used to determine products or services suitable for a user, comprising:
obtaining data and/or information about the user;
calculating a score that multidimensionally represents the user's characteristics based on the acquired data and/or information about the user;
method including.
(Item 18A)
19. The method of item 18, further comprising the features of any one or more of the above items.
(Item 19)
A program for calculating a score that is used to determine products or services suitable for a user, said program being executed in a computer system comprising a processor, said program comprising:
obtaining data and/or information about the user;
calculating a score that multidimensionally represents the user's characteristics based on the acquired data and/or information about the user;
A program that causes the processor to perform a process comprising:
(Item 19A)
20. The program of item 19, further comprising the features of any one or more of the above items.

According to the present invention, it is possible to highly accurately determine products or services suitable for the user by using a score that multidimensionally represents the user's characteristics, that is, the user's digital twin.

A diagram showing an example of a flow for determining a product or service suitable for a user A diagram showing an example of the configuration of a system 100 for determining products or services suitable for a user. A diagram showing an example of a specific configuration of a system 100 for determining products or services suitable for a user. Conceptual diagram showing association of information stored in the database unit 200 FIG. 4 is a diagram showing an example of the configuration of the processor unit 120; A diagram showing an example of the structure of a neural network model that can be used by the calculation means 122 FIG. 12 is a diagram showing an example of a configuration of a processor section 120', which is an alternative embodiment of the processor section 120; FIG. 12 is a diagram showing an example of a configuration of a processor section 120', which is an alternative embodiment of the processor section 120; FIG. 7 illustrates an example of processing (process 700) by system 100 for determining products or services suitable for a user;

Hereinafter, embodiments of the present invention will be described with reference to the drawings.

1. Service for Determining Products or Services Suitable for Users The inventors of the present invention have developed a new service for determining products or services suitable for users. This service makes use of a user's "digital twin" to determine which products or services are suitable for the user. Here, "digital twin" refers to a copy of an object in real space in virtual space or digital space. In other words, the user's "digital twin" reflects what kind of person the user is or what the user's character is in virtual space or digital space. Here, the 'digital twin' can be represented by a score that multidimensionally represents the features of the user. In other words, this score can represent what kind of person the user is, or what kind of person the user is.

This service uses the user's digital twin to determine products or services suitable for the digital twin. Since the generated digital twin reflects the personality of the user, a product or service suitable for the digital twin can in turn be regarded as a product or service suitable for the user.

Fig. 1 shows an example of the flow for determining products or services suitable for the user.

In this example, a computer system 100 of a provider that provides services for determining goods or services suitable for the user determines goods or services suitable for the user U.

In step S1, the user U provides data and/or information regarding the user U to the computer system 100.

Here, the data about users refers to objective information about users. The data regarding the user may be private information known only to the user, or may be public information shared with a third party. Data about the user may be obtained, for example, from the user's information terminal and/or an IoT device capable of communicating with the user's information terminal. Data about the user can be obtained from anywhere on the network N, for example. Data about the user can be recorded in a token stored in place on the network N, for example. Here, "token" refers to a physical or virtual container capable of holding information. Information held in a "token" is preferably held in the token in such a way that it is difficult to tamper with (eg, using blockchain technology). This makes the information held in the "token" valuable. Tokens may be subject to circulation, and may be treated as analogous to virtual currency, for example. Data related to the user includes, for example, the user's action history (for example, exercise history, health activity history, purchase history, saving action history, meal history, web page browsing history, conversation history with AI speaker, SNS usage history, etc.), Including, but not limited to, health information (eg, height, weight, blood pressure, heart rate, pre-existing diseases, etc.), DNA information, insurance coverage information, and the like. Data about a user may include the user's personal information. Examples of personal information include, but are not limited to, at least one of name, address, telephone number, email address, age, gender, family structure, occupation, annual income, assets, medical history, and the like.

Data about the user can be obtained, for example, from a payroll card. A payroll card is used in a payroll card system, which refers to a system capable of electronically receiving wage payments from an employer (eg, receiving electronic money). A payroll card may be a physical card or a virtual card (eg, an electronic wallet). Payroll cards allow workers to receive wage payments without going through a bank account. Payroll cards may record income and expense information from wage payments. In addition, the payroll card may also record information regarding business expenses. For example, the payroll card records the amount paid as an expense and the nominal amount. Therefore, by obtaining user-related data from the payroll card, not only the user's private income and expenditure information but also the user's business income and expenditure information can be used as the user-related data.

Here, user-related information refers to subjective information centered on the user. The information about the user may be private information known only to the user himself/herself, or may be public information shared with a third party. Information about the user can be obtained, for example, by direct input by the user into the information terminal device. Information about the user can be indirectly derived, for example, from interviews or questions of the user. Information about the user can be obtained from anywhere on the network N, for example. Information about the user can be recorded in a token stored in place on the network N, for example. The information about the user includes, but is not limited to, for example, hobbies, preferences, reasons for action (for example, reasons for applying for current or past jobs, reasons for changing jobs from past jobs, etc.).

The user U can provide data and/or information about the user U to the computer system 100, for example, via his/her own information terminal device. Alternatively, user U may provide data and/or information about user U to computer system 100 via an IoT device associated with user U, for example.

In addition to or in lieu of step S1, in step S2, the network N is configured so that the user U provides the computer system 100 with data and/or information about the user U stored on the network N. Send instructions on. For example, user U can send an instruction to provide computer system 100 with a token that is stored in place on the network.

At step S3, data and/or information about user U is provided to computer system 100 from a location on network N that stores data and/or information about user U. For example, user U's token is provided to computer system 100 from some location on the network.

When the computer system 100 receives data and/or information regarding the user U, the computer system 100 generates a "digital twin" of the user U based on the data and/or information regarding the user U. Here, the 'digital twin' can be represented by a score that multidimensionally represents the features of the user. The computer system 100 uses the generated "digital twin" of the user U to determine products or services suitable for the user U's "digital twin". Since the generated "digital twin" of user U reflects the personality of user U, goods or services suitable for the digital twin are, in turn, regarded as goods or services suitable for user U. be able to.

In step S4, the computer system 100 presents the user U with the determined product or service suitable for the user U. Thereby, the user U can know the goods or services suitable for him/herself.

Here, the aspect of presenting the user U with a product or service suitable for the user U can be any aspect. In one example, goods or services suitable for user U can be written to the token provided in step S3 before the token is provided to user U or returned to its place on the network. This updates the user U's token, enabling information on goods or services suitable for the user U to be used in subsequent processing. In another example, a product or service suitable for the user U may be sold to the user U. This allows the user U to purchase products or services suitable for him/herself.

The computer system 100 described above can be implemented by a system for determining products or services suitable for users, which will be described later.

2. Configuration of System for Determining Goods or Services Suitable for User FIG. 2A shows an example of the configuration of a system 100 for determining goods or services suitable for a user.

The system 100 is connected to the database unit 200. System 100 is also connected to at least one user terminal device 300 via network 400 .

Although three user terminal devices 300 are shown in FIG. 2A, the number of user terminal devices 300 is not limited to this. Any number of user terminals 300 may be connected to system 100 via network 400 .

Network 400 can be any type of network. Network 400 may be, for example, the Internet or a LAN. Network 400 may be a wired network or a wireless network.

An example of the system 100 is a computer (for example, a server device) installed in a provider that provides a service of recommending products or services, but is not limited to this. For example, it may be a computer located at a provider of goods or services. An example of the user terminal device 300 is a computer (for example, a terminal device) used by a user who is a consumer of goods or services, but is not limited to this. Here, the computer (server device or terminal device) can be any type of computer. For example, the terminal device can be any type of terminal device, such as smart phones, tablets, personal computers, smart glasses, smart watches, and the like.

The database unit 200 stores at least various types of information used to calculate a score that indicates user characteristics.

FIG. 2B shows an example of a specific configuration of system 100 for determining products or services suitable for a user.

The system 100 includes an interface section 110, a processor section 120, and a memory section 130.

The interface unit 110 exchanges information with the outside of the system 100. The processor unit 120 of the system 100 can receive information from outside the system 100 via the interface unit 110 and can transmit information outside the system 100 . The interface unit 110 can exchange information in any format.

The interface unit 110 includes, for example, an input unit that allows information to be input to the system 100 . It does not matter in what manner the input allows information to be entered into the system 100 . For example, if the input unit is a receiver, the receiver may receive information from outside the system 100 via a network for input. Alternatively, if the input unit is a data reader, information may be input by reading information from a storage medium connected to system 100 .

The interface unit 110 includes, for example, an output unit that enables information to be output from the system 100 . It does not matter in what manner the output unit allows information to be output from the system 100 . For example, if the output unit is a transmitter, the transmitter may output information by transmitting it to the outside of system 100 via a network. Alternatively, if the output unit is a data writing device, information may be output by writing information to a storage medium connected to system 100 .

The system 100 can send information to and/or receive information from the database unit 200 via the interface unit 110, for example. System 100 can transmit information to and/or receive information from user terminal 300 via interface 110, for example.

The system 100 can receive data and/or information regarding the user via the interface unit 110, for example. As will be described later, the data about the user is objective information about the user, and the information about the user is subjective information about the user.

The system 100 can receive, for example, via the interface unit 110, a request to determine suitable goods or services. A request to determine suitable goods or services may be included, for example, in a request to purchase any goods or services. System 100 can, for example, transmit information regarding the determined goods or services via interface unit 110 .

The processor unit 120 executes the processing of the system 100 and controls the operation of the system 100 as a whole. The processor unit 120 reads a program stored in the memory unit 130 and executes the program. This allows the system 100 to function as a system that executes desired steps. The processor unit 120 may be implemented by a single processor or multiple processors.

The memory unit 130 stores programs required for executing the processes of the system 100 and data required for executing the programs. The memory unit 130 may store a program for causing the processor unit 120 to perform processing for determining products or services suitable for the user (for example, a program for realizing processing shown in FIG. 7, which will be described later). . Here, it does not matter how the program is stored in the memory unit 130 . For example, the program may be pre-installed in memory unit 130 . Alternatively, the program may be installed in memory unit 130 by being downloaded via a network. In this case, the type of network does not matter. Memory unit 130 may be implemented by any storage means.

The database unit 200 can store, for example, data and/or information about users.

　The data about the user is objective information about the user. The data regarding the user may be private information known only to the user, or may be public information shared with a third party. Data about the user may be obtained, for example, from the user's information terminal and/or an IoT device capable of communicating with the user's information terminal. Data about the user can be obtained from anywhere on the network N, for example. Data about the user can be recorded in a token stored in place on the network N, for example. Data related to the user includes, for example, the user's action history (for example, exercise history, health activity history, purchase history, saving action history, meal history, web page browsing history, conversation history with AI speaker, SNS usage history, etc.), Including, but not limited to, health information (eg, height, weight, blood pressure, heart rate, pre-existing diseases, etc.), DNA information, insurance coverage information, and the like. Data about a user may include the user's personal information. Examples of personal information include, but are not limited to, at least one of name, address, telephone number, email address, age, gender, family structure, occupation, annual income, assets, medical history, and the like.

Data about the user can be obtained, for example, from a payroll card. A payroll card is a system that allows you to receive wage payments electronically from your employer. Payroll cards allow workers to receive wage payments without going through a bank account. Payroll cards may record income and expense information from wage payments. In addition, the payroll card may also record information regarding business expenses. For example, the payroll card records the amount paid as an expense and the nominal amount. Therefore, by obtaining user-related data from the payroll card, not only the user's private income and expenditure information but also the user's business income and expenditure information can be used as the user-related data.

Information about users is subjective information centered on users. The information about the user may be private information known only to the user himself/herself, or may be public information shared with a third party. Information about the user can be obtained, for example, by direct input by the user into the information terminal device. Information about the user can be indirectly derived, for example, from interviews or questions of the user. Information about the user can be obtained from anywhere on the network N, for example. Information about the user can be recorded in a token stored in place on the network N, for example. The information about the user includes, but is not limited to, for example, hobbies, preferences, reasons for action (for example, reasons for applying for current or past jobs, reasons for changing jobs from past jobs, etc.).

The database unit 200 stores various types of information used to calculate scores that represent user characteristics.

Here, the user's characteristics can be a concept that represents what kind of person the user is, that is, the user's personality. User characteristics can also represent a user's personality in terms of whether the user is trustworthy. For example, a user's characteristics may describe the user in terms of "personality" and/or may describe the user in terms of "money" and/or may describe the user in terms of "health". It is possible to express the personality of the user from the viewpoint of Therefore, a score that multidimensionally represents a user's characteristics can include a feature amount related to "personality" and/or a feature amount related to "money" and/or a feature amount related to "health". The multi-dimensional axes of score can include, for example, a "personality" aspect axis, a "money" aspect axis, and/or a "health" aspect axis. Here, "personality" is information indicating whether or not a person's character and/or personality is trustworthy, and includes, for example, evaluations from others. "Money" is information about the person's money, and includes information such as annual income, assets, and whether the person is saving. "Health" is information about the health of the person, and includes, for example, information such as disease, eating habits, activities for health, and the like.

As an example, when the score representing the characteristics of the user has an axis of viewpoints related to "personality", points are assigned to multiple items related to "personality", and based on the scores of each of the multiple items, the score related to "personality" A person's character will be expressed. For example, in a person who has an angry but honest personality, among the items related to "personality", the score of the item related to "honest person" is high or the score related to "liar" is low, and the score related to "impatient" Higher scores for items or lower scores for items related to "patience".

As another example, when the score representing the characteristics of the user has an axis of viewpoints related to "money", points are assigned to multiple items related to "money", and based on the scores of each of the multiple items, the "money" The personality of the person will be expressed in relation to. For example, among items related to "money," a person who spends a lot of money has a high score for an item related to "thrifty person" or a low score for an item related to "thrifty person."

As another example, when the score representing the characteristics of the user has an axis of viewpoints related to "health", points are given for a plurality of items related to "health", and based on the scores of each of the multiple items, "health" The personality of the person will be expressed in relation to. For example, a person with an obese body type has a high score for items related to "degree of obesity" or "BMI" among items related to "health."

These items are examples, and each multidimensional axis can have any item. As the number of items on each axis increases, the character of the user can be expressed in more detail with respect to the viewpoint of that axis, but the amount of data increases accordingly. The smaller the number of items on each axis, the smaller the amount of score data representing the characteristics of the user and the easier it is to handle, but the more roughly the personality of the user is represented. For example, the score representing the characteristics of the user is specified for at least two of the multiple axes from the scores represented for the multiple axes having a large number of items (scores representing the personality of the user in detail). It may be a score derived by extracting items of (a score that roughly represents the personality of the user).

For example, the database unit 200 may store concepts related to parameters used for score calculation and various information in association with each other. Parameters used for score calculation may include, for example, “personality,” “money,” and “health.”

For example, for the parameter "personality", the database unit 200 may store the concept of personality and various pieces of information in association with each other. For example, the concept of "altruistic" (or "transpersonal") related to "personality" includes keywords such as "volunteer", "donation", "caring", "consultation", "frequently participate in volunteering", Statuses such as "frequently consulted" and "has contributed" can be stored in association with each other. For example, the concept of 'SDGs' (Sustainable Development Goals, or 'Sustainable Development Goals') in relation to 'personality' includes keywords related to 17 goals and/or 169 targets (e.g., 'equality', 'environmental Conservation”, etc.), “I am taking actions related to 17 goals and/or 169 targets”, “I have thoughts related to 17 goals and/or 169 targets”, etc. can be stored as

For example, for the parameter "money", the database unit 200 may store the concept of money and various information in association with each other. For example, the concept of "rich" (or "cash flow rich") related to "money" is stored in association with keywords such as "Harvard University" and "MBA" and status such as "annual income of 20 million yen or more". can be For example, the concept of "rich" (or "stock rich") related to "money" can be stored in association with keywords such as "landlord" and "shareholder" and status such as "asset of 100 million yen or more". .

For example, for the parameter "health", the database unit 200 may store concepts related to health and various information in association with each other. For example, in the concept of "good body" (or "physical health") related to "health", keywords such as "non-smoking" and "normal blood pressure", statuses such as "BMI 18-27", etc. are associated and stored. obtain. For example, the concept of "mindfulness" (or "mental health") related to "health" includes keywords such as "rewarding" and "stressless", "stress check result is less than a predetermined value", and "sense of coherence". (SOC: Sense of coherence) is high” and the like can be associated and stored.

FIG. 2C is a conceptual diagram showing association of information stored in the database unit 200. FIG.

In the example shown in FIG. 2C, keywords such as "Harvard University", "Stanford University", "MBA", "doctor", and "manager" are associated with the concept of "rich" related to money. ing. In addition, other keywords (eg, "business school", "practitioner", "listed company", etc.) are associated with these keywords. Further, further keywords may be associated with these keywords.

Such associations can be made, for example, using artificial intelligence (AI) capable of performing semantic searches, that is, artificial intelligence that has learned correlations between keywords.

This artificial intelligence learns the correlation between keywords from a large amount of sentences. For example, the artificial intelligence parses the text to extract multiple keywords in the text and identify relationships between the multiple keywords in the text. For example, if a certain keyword is used together with another certain keyword in many sentences, this artificial intelligence learns those keywords as having a strong correlation. The artificial intelligence that has learned the correlation between keywords in this way can output keywords that are correlated with the input keyword based on the learned correlation.

For example, if the keyword "rich" is entered into the artificial intelligence that has learned the correlation between keywords, "Harvard University", "Stanford University", "MBA", and "doctor" are correlated with "rich". , “manager”, etc. may be output. For example, if you input the keywords "rich" and "doctor" into AI that has learned the correlation between keywords, the keywords correlated with "rich" and "doctor" will be "practitioner", "clinician", etc. can be output.

In this way, in the database unit 200, keywords input to artificial intelligence that has learned correlations between keywords and output keywords can be associated and stored.

In the examples shown in FIGS. 2A and 2B, the database unit 200 is provided outside the system 100, but the present invention is not limited to this. It is also possible to provide at least part of the database unit 200 inside the system 100 . At this time, at least part of the database section 200 may be implemented by the same storage means as the storage means implementing the memory section 130, or may be implemented by a storage means different from the storage means implementing the memory section 130. may In any event, at least a portion of database unit 200 is configured as a storage unit for system 100 . The configuration of database unit 200 is not limited to a specific hardware configuration. For example, the database unit 200 may be configured with a single hardware component, or may be configured with a plurality of hardware components. For example, the database unit 200 may be configured as an external hard disk device of the system 100, may be configured as storage on the cloud connected via a network, or may be configured as a distributed type using blockchain technology or the like. It may be configured as a network.

For example, user-related data and/or information is stored in a database unit 200 configured as a distributed network that uses blockchain technology or the like, and falsification of the user-related data and/or information is substantially impossible. It becomes possible. This ensures the reliability of the data and/or information regarding the user.

3 shows an example of the configuration of the processor unit 120. FIG.

The processor unit 120 includes acquisition means 121 , calculation means 122 and determination means 123 .

The acquisition means 121 is configured to acquire data and/or information regarding the user.

For example, the acquisition means 121 may acquire data and/or information about the user stored in the database section 200 via the interface section 110 . Alternatively, the acquisition means 121 may acquire data and/or information about the user received from the user terminal device 300 via the interface section 110, for example. Alternatively, the acquisition means 121 may acquire data and/or information regarding the user from a given location on the network. Acquisition means 121 can acquire data about the user from, for example, a payroll card possessed by the user (or a server device that manages the payroll card). The obtained data and/or information about the user is passed to the computing means 122 .

The calculation means 122 is configured to calculate a score that multidimensionally represents the features of the user based on data and/or information about the user. A user's characteristics can be a concept that represents what kind of person the user is, that is, the user's personality. User characteristics can also represent a user's personality in terms of whether the user is trustworthy. For example, a user's characteristics may describe the user in terms of "personality" and/or may describe the user in terms of "money" and/or may describe the user in terms of "health". It is possible to express the personality of the user from the viewpoint of Therefore, a score that multidimensionally represents a user's characteristics can include a feature amount related to "personality" and/or a feature amount related to "money" and/or a feature amount related to "health". The term “digital twin” is well known, and a score that multidimensionally represents a user's characteristics can also be said to be a score that represents the user's digital twin.

The multidimensional axis of the score can include, for example, the axis of the viewpoint regarding "personality", the axis of the viewpoint regarding "money", and/or the axis of the viewpoint regarding "health".

A score that multidimensionally represents a user's characteristics can include, for example, a feature quantity that can be used to evaluate whether the user is a more trustworthy person. For example, the more trustworthy a user's character and/or personality is, the more the feature amount related to "personality" that indicates that the user is a trustworthy person is included in the score representing the user's characteristics in a multidimensional manner. become. For example, the more trustworthy the user's thoughts, actions and/or states regarding money are, the more the feature amount related to "money" indicating that the user is trustworthy in the score that multidimensionally represents the characteristics of the user. A lot will be included. For example, the more reliable the user's thinking, behavior, and/or state regarding health, the more the feature amount related to "health" indicating that the user is a trustworthy person in the score representing the user's characteristics in a multidimensional manner. A lot will be included.

In one embodiment, the calculation means 122 can calculate the score, for example, depending on the extent to which the data and/or information about the user are correlated with the parameters used to calculate the score. The calculation means 122, for example, refers to the database unit 200 that stores various information correlated with the concept of the parameters used to calculate the score, and the data and/or information about the user is related to the parameters used to calculate the score. Determine the degree of correlation with the concept. Data and/or information about the user may include, for example, information about "personality," "money," and "health."

The calculation means 122 can, for example, determine the degree of correlation between data and/or information about the user and the concept of "personality" and calculate a score according to the determined degree of correlation. For example, if the data and/or information about the user are highly correlated with a particular concept among the concepts about "personality", the calculation means 122 includes the feature amount corresponding to that concept in the score or corresponds to that concept. It is possible to increase the score of the item. For example, the more strongly data and/or information about a user is correlated with "altruistic" (or "transpersonal"), the more or more features corresponding to "altruistic" are included in the score or "altruistic". ” can be increased. For example, the data and / or information about the user is more strongly correlated with "SDGs" (or "Sustainable Development Goals"), the more or more features corresponding to "SDGs" are included in the score or "SDGs ” can be increased.

The calculation means 122 can, for example, determine the degree of correlation between data and/or information about the user and the concept of "money" and calculate a score according to the determined degree of correlation. For example, when the data and/or information about the user are highly correlated with a specific concept among the concepts about "money", the calculation means 122 includes the feature quantity corresponding to the concept in the score or corresponds to the concept. It is possible to increase the score of the item. For example, the more strongly the data and/or information about the user is correlated with "cash flow rich", the more or more features corresponding to "cash flow rich" are included in the score or the concept of "cash flow rich". The score of the corresponding item can be increased. For example, the more strongly the data and/or information about the user correlates with "stock rich," the more or more features corresponding to "stock rich" are included in the score, or the items corresponding to the concept of "stock rich." score can be increased.

The calculation means 122 can, for example, determine the degree of correlation between data and/or information regarding the user and the concept regarding "health" and calculate a score according to the determined degree of correlation. For example, when the data and/or information about the user are highly correlated with a specific concept among the concepts about "health", the calculation means 122 includes the feature quantity corresponding to the concept in the score or corresponds to the concept. It is possible to increase the score of the item. For example, the more strongly the data and/or information about the user correlates with "excellent body" (or "physical health"), the more or more features corresponding to "excellent body" are included in the score or "excellent body". Items corresponding to the concept of "body" can be scored higher. For example, the more strongly the data and/or information about the user is correlated with "mindfulness" (or "mental health"), the more the feature quantity corresponding to "mindfulness" (or "mental health") becomes. Items that are more or more strongly included in the score or correspond to the concept of "mindfulness" can be scored higher.

In one embodiment, the calculation means 122, for example, uses a machine learning model that has learned the relationship between data and/or information about a plurality of users and the score, to calculate a score that multidimensionally represents the characteristics of the user. be able to. Data and/or information about the user may include, for example, information about "personality," "money," and "health."

A machine learning model can be constructed using any machine learning model. A machine learning model can be, for example, a neural network model.

FIG. 4 shows an example of the structure of a neural network model that can be used by the calculation means 122.

A neural network model has an input layer, at least one hidden layer, and an output layer. The number of nodes in the input layer of the neural network model corresponds to the number of dimensions of input data and/or information about the user. The hidden layer of a neural network model can contain any number of nodes. The number of nodes in the output layer of the neural network model corresponds to the number of dimensions of output data. For example, when outputting a score that multidimensionally represents a user's characteristics, the number of nodes in the output layer may be one.

The neural network model can undergo learning processing in advance using the data and/or information regarding the user acquired by the acquisition means 121 . The learning process is a process of calculating the weight coefficient of each node of the hidden layer of the neural network model using the data and/or information regarding the user previously acquired by the acquisition means 121 .

The learning process is, for example, supervised learning. In supervised learning, for example, data and/or information about a user is used as input training data, and a multi-dimensional score representing the characteristics of the user is used as output training data, and information of a plurality of users is used to create a neural network. By calculating a weighting factor for each node in the model's hidden layer, a machine learning model can be constructed that can correlate data and/or information about a user with a score.

For example, a set of (teaching data for input, teaching data for output) for supervised learning consists of (data and/or information about the first user, score of the first user), (data about the second user and/or information, the score of the second user), . When data and/or information about the user newly acquired from the user is input to the input layer of such a trained neural network model, the user's score is output to the output layer.

In supervised learning, for example, the degree of correlation between data and/or information about the user and the parameters used to calculate the score can be used as input teacher data.

The calculated score is passed to the determining means 123.

Refer to Figure 3 again. The determination means 123 is configured to use the scores calculated by the calculation means 122 to determine products or services suitable for the user. In one example, a specific product or service is associated with each of a plurality of feature amounts that can be included in the score, and the determining means 123 selects a product or service corresponding to the feature amount included in the score as a product suitable for the user. Or it can be determined as a service.

In another example, the determining means 123 can derive a specific feature amount from the score and determine a product or service based on the derived feature amount. For example, a specific product or service is associated with each of a plurality of feature quantities that can be derived from the score, and the determining means 123 selects a product or service corresponding to the feature quantity derived from the score as a product suitable for the user. Or it can be determined as a service. For example, a specific product or service is associated with each combination of some of the feature amounts that can be derived from the score, and the determining means 123 determines the combination of feature amounts derived from the score. A corresponding product or service can be determined as a product or service suitable for the user.

The determining means 123 can derive a specific feature amount from the score by any method. In one example, the determining means 123 can extract a specific feature amount from the feature amounts included in the score. Here, the extraction of the feature amount means extracting the feature amount as it is without changing the value of the feature amount. In another example, the determining means 123 can derive a specific feature amount by applying a specific function to the score or the feature amount included in the score. Here, the specified function can be any function. For example, a particular function can be a hash function.

　The features extracted from the score can be changed according to the field of the product or service to be determined. For example, the determination means 123 can extract a specific feature amount according to the field of the determined product or service from the feature amounts included in the score. For example, the determination means 123 can derive a specific feature amount by applying a specific function according to the field of the product or service to be determined to the score or the feature amount included in the score. By tuning the feature amount derived from the score for the specific product or service field, the system 100 can determine the product or service suitable for the user for the specific product or service. Become.

A system for determining suitable products or services for a user is tuned to determine, for example, financial products suitable for the user, insurance products suitable for the user, travel plans suitable for the user, consumer goods suitable for the user. can be Note that the system of the present invention is not limited to determining these goods or services, and can be used to determine any other goods or services.
(System for determining financial products suitable for users)

In one example, system 100 can be configured as a system for determining financial products suitable for a user. Here, a financial product means a product or a group of products handled in financial transactions. Financial products are, for example, products handled by banks, insurance companies, securities companies, etc., and include deposits, investment trusts, insurance products, stocks, government bonds, foreign exchange, loans, virtual currencies, commodities, or combinations thereof.

In this system, the decision means 123 can use the scores to decide financial products suitable for the user. The determining means 123 can derive useful feature quantities from the scores for determining financial products suitable for the user. For example, the determining means 123 can extract useful feature amounts for determining financial products suitable for the user from the feature amounts included in the scores. For example, the determining means 123 applies a function specific to the field of financial products to the score or the feature values included in the score, thereby deriving useful feature values for determining financial products suitable for the user. can be done.

Features that are useful for determining financial products suitable for the user preferably include features that can assess the user's risk. This is because the purchaser's risk assessment has a great influence on the decision of the financial product, and therefore, the financial product to be decided can be more suitable for the user by considering the user's risk. The feature quantity that can evaluate the user's risk may be, for example, a feature quantity empirically used in the financial industry, or a feature quantity derived by machine learning (for example, cluster analysis, etc.). good too.
For example, if a feature value useful for determining a financial product suitable for a user indicates that the user is willing to take risks, a high-risk, high-return financial product is suitable for the user. It can be determined to be a financial instrument.

(System for determining insurance products suitable for users)
In one example, system 100 can be configured as a system for determining suitable insurance products for a user. An insurance product has various conditions such as coverage, premium rates, and riders. Determining the insurance product suitable for the user also includes determining various conditions such as coverage, premium rate, and rider.

In this system, the determining means 123 can use the scores to determine insurance products suitable for the user. The determining means 123 can derive useful feature amounts from the scores for determining insurance products suitable for the user. For example, the determining means 123 can extract useful feature amounts for determining insurance products suitable for the user from the feature amounts included in the score. For example, the determining means 123 applies a function specific to the insurance product field to the score or the feature quantity included in the score, thereby deriving a feature quantity useful for determining an insurance product suitable for the user. can be done.

Features useful for determining a suitable insurance product for a user preferably include features capable of assessing the user's health and/or financial risks. Since the subscriber's health and financial risks have a significant impact on insurance product decisions, the health and/or financial risks of the user are considered to make the determined insurance product more suitable for the user. because it can become The feature quantity that can evaluate the user's risk may be, for example, a feature quantity empirically used in the insurance industry, or a feature quantity derived by machine learning (for example, cluster analysis, etc.). For example, if a feature value useful for determining an insurance product suitable for a user indicates that the user is not exercising enough, the user has a cancer insurance rider attached to the medical insurance. An insurance product may be determined to be the preferred insurance product.

(System for determining travel plans suitable for users)
In one example, the system 100 can be configured as a system for determining suitable travel plans for a user. A travel plan has various conditions such as destinations, means of transportation, contents of activities, and time schedules. Determining a travel plan suitable for the user also includes determining various conditions such as the destination, means of transportation, content of activities, and time schedule.

In this system, the determination means 123 can use the score to determine a travel plan suitable for the user. The determining means 123 can derive useful feature quantities from the score for determining a travel plan suitable for the user. For example, the determining means 123 can extract useful feature amounts for determining a travel plan suitable for the user from the feature amounts included in the score. For example, the determining means 123 applies a function specific to the travel plan field to the score or the feature included in the score to derive a feature that is useful for determining a travel plan suitable for the user. can be done.

Feature quantities useful for determining a travel plan suitable for the user preferably include feature quantities that can evaluate the user's behavioral characteristics. This is because the traveler's behavioral characteristics have a great influence on the determination of the travel plan, and therefore, by considering the user's behavioral characteristics, the determined travel plan can be more suitable for the user. The feature quantity that can evaluate the behavioral characteristics of the user may be, for example, a feature quantity empirically used in the travel industry, or a feature quantity derived by machine learning (for example, cluster analysis, etc.). may be
For example, if a feature value that can evaluate a user's behavioral personality indicates that the user is adventurous, a travel plan that includes an activity to explore nature is suitable for the user. can be determined to be

(System for determining consumer goods suitable for users)
In one example, system 100 can be configured as a system for determining suitable consumer goods for a user. Consumer goods are products purchased by consumers for the purpose of consumption within the household budget. Consumer goods include durable consumer goods that are used over a long period of time and non-durable consumer goods that are consumed for a single use.

In this system, the determining means 123 can use the scores to determine consumer goods suitable for the user. The determining means 123 can derive useful features from the scores for determining consumer goods suitable for the user. For example, the determining means 123 can extract useful feature amounts for determining consumer goods suitable for the user from the feature amounts included in the score. For example, the determining means 123 applies a function specific to the field of consumer goods to the score or the feature amount included in the score to derive useful feature amounts for determining consumer goods suitable for the user. can be done.

Features useful for determining consumer goods suitable for a user preferably include features capable of evaluating user preferences. This is because the consumer's taste has a great influence on the decision of the consumer goods, and thus the determined consumer goods can be more suitable for the user by considering the user's taste. The feature amount that can evaluate the user's risk may be, for example, a feature amount empirically used in the consumer goods manufacturing or sales industry, or derived by machine learning (e.g., cluster analysis, etc.) It may be a feature amount.
For example, if a feature value that can evaluate a user's taste indicates that the user likes old-fashioned things, it is determined that consumer goods with designs that were popular in the past are suitable for the user. can be

Products or services suitable for the user determined by the determining means 123 are output from the processor section 120 to the outside of the system 100 via the interface section 110, for example. This output can be used for any purpose. For example, the determined user-preferred goods or services can be presented to the user. Alternatively, for example, only information indicating products or services suitable for the determined user can be presented to the user. Alternatively, for example, the user may be presented with product or service recommendations based on the determined user-preferred product or service.

FIG. 5 shows an example of the configuration of a processor section 120 ′, which is an alternative embodiment of the processor section 120 .

The processor section 120 ′ has the same configuration as the processor section 120 except that it includes an adjusting means 124 . In FIG. 5, the same reference numerals are given to the same components as those in the configuration described above with reference to FIG. 3, and the description thereof is omitted here.

The processor unit 120 ′ includes acquisition means 121 , calculation means 122 , determination means 123 and adjustment means 124 .

The adjusting means 124 is configured to adjust the range in which the acquiring means 121 can acquire data and/or information about the user. It does not matter how the adjusting means 124 adjusts the available range of data and/or information about the user. For example, the adjusting means 124 can adjust the obtainable range of user-related data and/or information by specifying user-related data and/or information that the obtaining means 121 is prohibited from obtaining. For example, the adjusting means 124 can adjust the obtainable range of user-related data and/or information by specifying the user-related data and/or information that the obtaining means 121 is permitted to obtain.

For example, the user can specify the obtainable range of data and/or information related to the user. Adjustment means 124 can adjust the available range of data and/or information about the user in response to input by the user. This allows the user to provide or disclose to the system 100 only data and/or information that the user wishes to reflect in score calculation, for example.

The calculation means 122 can calculate the score according to the obtainable range adjusted by the adjustment means 124 . From a system standpoint, it is preferable to have the user disclose all available data and/or information. This is because all available information can be used to calculate the score, which improves the accuracy of the score and thus the accuracy of the determined product or service. However, from the standpoint of the user, there may be information that the user wishes to keep confidential. Therefore, in the system 100, the user is encouraged to disclose information by providing the user with an incentive corresponding to the obtainable range. As an incentive, for example, the calculating means 122 can calculate a score that gives preferential treatment to a user with a wider range of information acquisition. For example, the calculation means 122 can calculate the score so that the feature amount of honesty is included in the score of the user whose information can be acquired in a wide range. In one example, as an incentive, the calculation means 122 may calculate a score that gives preferential treatment to users who disclose information stored on their payroll cards compared to users who do not.

For example, the determination means 123 can determine the preferential product or service for the user having the preferential score calculated by the calculation means 122 as the preferred product or service. Preferential products or services include, for example, financial products with preferential transaction conditions, insurance products with preferential subscription conditions, travel plans with discounted prices, consumer goods with discounted prices, etc. Not limited.

In this way, the wider the range of information acquisition, the more preferential treatment will be given to products or services suitable for the determined user, so the user can be encouraged to provide or disclose a wider range of information.

FIG. 6 shows an example of the configuration of a processor section 120', which is an alternative embodiment of the processor section 120. FIG.

The processor unit 120 ″ has the same configuration as the processor unit 120 except that it includes a writing means 125 . In FIG. 6, the same reference numerals are given to the same components as those in the configuration described above with reference to FIG. 3, and the description thereof is omitted here.

The processor unit 120 ″ includes acquisition means 121 , calculation means 122 , determination means 123 and writing means 125 . The processor portion 120'' may further comprise adjustment means 124 as described above with reference to FIG.

The writing means 125 is configured to write the score calculated by the calculating means 122 into the token. The score-written token can be output from the processor unit 120 ″ to the outside of the system 100 via the interface unit 110 , for example. As a result, the calculated score can be used outside the system 100 via the token.

Here, the token is preferably a token whose owner has been specified (that is, a token with identity verification or KYC (Know Your Customer) completed). This is because a KYC-completed token guarantees that the information held in the token belongs to that person, and the reliability of the information can be enhanced. At this time, there is no need to disclose ownership of the token, and identity verification or KYC can be done with zero-knowledge illumination.

The score-written token may be provided to the determination means 123, for example, and the written score may be read by the determination means 123. In this case, the calculating means 122 and the determining means 123 do not need to communicate directly, and can exchange scores via tokens.

The writing means 125 may be configured to write information indicating the goods or services suitable for the user determined by the determining means 123 into the token. A token in which information indicating a product or service suitable for the user is written can be output from the processor unit 120 ″ to the outside of the system 100 via the interface unit 110 , for example. This makes it possible to use information indicating products or services suitable for the user even outside the system 100 via the token. For example, if the token is a KYC-completed token, it is guaranteed that the information indicating the preferred product or service belongs to the owner of the token, and is used outside the system 100 as trusted information. can be done.

In the above example, it was explained that the determination means 123 determines the product or service suitable for the user using the score calculated by the calculation means 122, but in the system 100, the determination means 123 is omitted. may That is, in the system 100 , the score calculated by the calculator 122 can be output from the processor section 120 to the outside of the system 100 via the interface section 110 . As a result, the calculated score can be used outside the system 100 via the token. For example, the calculated score can be sold to consumers (for example, financial institutions, insurance companies, etc.) as transaction targets.

In the examples shown in FIGS. 3, 5, and 6 described above, each component of the processor unit 120 is provided within the same processor unit 120, but the present invention is not limited to this. It is also within the scope of the present invention for each component of processor portion 120 to be distributed across multiple processor portions. At this time, the plurality of processor units may be located within the same hardware component, or may be located within separate nearby or remote hardware components.

Each component of the system 100 described above may be composed of a single hardware part, or may be composed of a plurality of hardware parts. When configured with a plurality of hardware components, it does not matter how the hardware components are connected. Each hardware component may be connected wirelessly or by wire. The system 100 of the present invention is not limited to any particular hardware configuration. It is within the scope of the present invention for the processor portion 120 to be configured with analog circuitry rather than digital circuitry. The configuration of the system 100 of the present invention is not limited to those described above as long as its functions can be realized.

3. Process by System for Determining Goods or Services Preferred for User FIG. 7 shows an example process (process 700) by system 100 for determining goods or services suitable for a user. Process 700 is performed in processor portion 120 of system 100 . Process 100 can be performed by similar processes in processor portion 120' and processor portion 120''.
In step S701, the acquisition unit 121 of the processor unit 120 acquires data and/or information regarding the user.
Acquisition means 121 may acquire data and/or information about a user stored in database section 200 via interface section 110, for example. Alternatively, the acquisition means 121 may acquire data and/or information about the user received from the user terminal device 300 via the interface section 110, for example. Alternatively, the acquisition means 121 may acquire data and/or information regarding the user from a given location on the network. Acquisition means 121 can acquire data about the user from, for example, a payroll card possessed by the user (or a server device that manages the payroll card). The obtained data and/or information about the user is passed to the computing means 122 .

In step S702, the calculation unit 122 of the processor unit 120 calculates a score that multidimensionally represents the characteristics of the user based on the data and/or information regarding the user acquired in step S701.
The calculating means 122 can calculate the score according to, for example, the extent to which the data and/or information about the user are correlated with the parameters used to calculate the score. The calculation means 122, for example, refers to the database unit 200 that stores various information correlated with the concept of the parameters used to calculate the score, and the data and/or information about the user is related to the parameters used to calculate the score. Determine the degree of correlation with the concept. Data and/or information about the user may include, for example, information about "personality,""money," and "health."

The calculation means 122 can, for example, determine the degree of correlation between data and/or information about the user and the concept of "personality" and calculate a score according to the determined degree of correlation. The calculation means 122 can, for example, determine the degree of correlation between the data and/or information regarding the user and the concept regarding "money" and calculate a score according to the determined degree of correlation. The calculating means 122 can, for example, determine the extent to which the data and/or information about the user correlates with the concept about "health", and calculate a score according to the determined extent of correlation.

The calculation means 122 can calculate a score that multidimensionally represents a user's characteristics, for example, using a machine learning model that has learned the relationship between data and/or information about a plurality of users and scores. Data and/or information about the user may include, for example, information about "personality," "money," and "health."

For example, inputting the data and/or information about the user acquired in step S701 into the machine learning model can output the user's score.

At step S703, the determining means 123 of the processor unit 120 uses the score calculated at step S702 to determine products or services suitable for the user. For example, the determining means 123 can determine the product or service corresponding to the feature amount included in the score as the product or service suitable for the user. Alternatively, the determining means 123 can derive a specific feature amount from the score and determine a product or service based on the derived feature amount.

In step S703, the determining means 123 can derive a specific feature amount from the score by any method. In one example, the determining means 123 can extract a specific feature amount from the feature amounts included in the score. In another example, the determining means 123 can derive a specific feature amount by applying a specific function to the score or the feature amount included in the score.

Thus, process 700 utilizes the user's digital twin to determine products or services suitable for the user. The more accurate the user's digital twin, the more accurate the determined user-preferred goods or services.

The product or service suitable for the user determined by process 700 can be used for any purpose. For example, the determined user-preferred goods or services can be presented to the user. Alternatively, the determined user-preferred goods or services can be written to the user's token. Alternatively, the determined user-preferred product or service can be used to develop another new product or service.

Although the example described above with reference to FIG. 7 describes that the processes are performed in a particular order, the order of each process is not limited to that described and may be performed in any order that is logically possible. obtain.

In the example described above with reference to FIG. 7, it was explained that the processing of each step shown in FIG. Not limited. At least one of the processes of each step shown in FIG. 7 may be implemented by a hardware configuration such as a control circuit.

In the above example, the system 100 is a computer (eg, server device) installed in a service provider that provides services for determining products or services suitable for users. The invention is not so limited. System 100 may be any information processing device that includes a processor unit.

The present invention is not limited to the embodiments described above. It is understood that the invention is to be construed in scope only by the claims. It is understood that a person skilled in the art can implement an equivalent range from the description of specific preferred embodiments of the present invention based on the description of the present invention and common technical knowledge.

The present invention provides a system that can determine products or services suitable for the user with high accuracy by using a score that multidimensionally represents the user's characteristics, that is, the user's digital twin. Useful.

100 System 200 Database Unit 300 User Device 400 Network

Claims

A system for determining products or services suitable for a user, comprising:
obtaining means for obtaining data and/or information about a user;
calculation means for calculating a score that multidimensionally represents the features of the user based on the acquired data and/or information about the user;
and determining means for using said score to determine suitable goods or services for said user.
The determining means is
Deriving a feature amount according to the field of the product or the service from the score;
2. The system according to claim 1, further comprising: determining said product or said service based on said feature quantity.
The system is a system for determining financial products suitable for a user,
3. The system according to claim 2, wherein said feature amount includes a feature amount capable of evaluating said user's risk.
The system is a system for determining insurance products suitable for a user,
4. The system according to claim 3, wherein said feature quantity includes a feature quantity capable of evaluating health and/or financial risks of said user.
The system is a system for determining a travel plan suitable for the user,
3. The system according to claim 2, wherein said feature amount includes a feature amount capable of evaluating behavioral characteristics of said user.
The system is a system for determining consumer goods suitable for a user,
3. The system according to claim 2, wherein said feature amount includes a feature amount capable of evaluating said user's preference.
The system according to any one of claims 1 to 6, wherein the score includes a feature quantity that can be used to evaluate whether the user is a trustworthy person.
The system according to claim 7, wherein the score includes a feature amount related to personality, a feature amount related to money, and a feature amount related to health.
The calculating means calculates the score according to the extent to which the acquired data and/or information about the user correlates with at least one of a concept about personality, a concept about money, and a concept about health. 9. The system according to any one of clauses 8.
further comprising adjusting means for adjusting a range in which the obtaining means can obtain data and/or information about the user;
The system according to any one of claims 1 to 8, wherein said calculating means calculates said score according to said obtainable range.
The system according to any one of claims 1 to 10, wherein said data and/or information comprises information stored on a payroll card.
further comprising writing means for writing the score to a token owned by the user;
A system according to any of claims 1-11, wherein said determining means are arranged to be able to read said score from said token.
13. The system of claim 12, wherein said writing means is further configured to write information indicative of said determined goods or services into said token.
The system according to any one of claims 1 to 13, further comprising presenting means for presenting the determined product or service to the user.
A method for determining products or services suitable for a user, comprising:
obtaining data and/or information about the user;
calculating a score that multidimensionally represents the user's characteristics based on the acquired data and/or information about the user;
and using said score to determine suitable goods or services for said user.
A program for determining goods or services suitable for a user, said program being executed on a computer system comprising a processor, said program comprising:
obtaining data and/or information about the user;
calculating a score that multidimensionally represents the user's characteristics based on the acquired data and/or information about the user;
and determining products or services suitable for the user using the score.
A system for calculating a score used to determine products or services suitable for a user,
obtaining means for obtaining data and/or information about a user;
calculation means for calculating a score that multidimensionally represents the features of the user based on the acquired data and/or information about the user;
A system with